Linear Regression

data = transpose(dataT)
function lr(pw){
 return(
  Plot.plot({
  marks: [
    Plot.dot(data, {x: "x", y: "y"}),
    Plot.line(curve(data), {x: "x", y: "y"}),
    Plot.ruleX([0], {stroke: "gray"}),  // Add a vertical line at x=0
    Plot.ruleY([80], {stroke: "gray"})  
  ],
  y: {
    label: "Blutdruck",
    domain: [80,200],
    labelFontSize: 50  
  },
  x: {
    label: "Alter",
    domain: [0,80]
  },
  width: pw,
  height: pw/1.5,
  color: "steelblue"
})
)
}


function curve(template){
  const ages = template.map(d => d.x);
  const [minAge, maxAge] = d3.extent(ages);
  const xvals = d3.range(minAge, maxAge, (maxAge - minAge) / 100); 
  let yhat = xvals.map(x => w*x + b);
  return xvals.map((x, i) => ({x, y: yhat[i]})); //Thanks ChatGPT
}

lr(900)

//Redoing the plot
lr(800)

//Redoing the plot
lr(800)

Inputs.bind(Inputs.range([-10, 120]), viewof b);

Inputs.bind(Inputs.range([-5, 5]), viewof w);

html`<br>y = ${w} &middot; x + ${b}<br>`

function color(value) {
  return  d3.scaleLinear().domain([-1, 0, 1]).range(['blue', 'gray', 'green'])(value);
}
function lw(value) {
  return d3.scaleLinear().domain([-1, 1]).range([0.1, 1])(value);
}

dot`${my_graph}`

my_graph = `digraph {
  node [fontsize=20, fontname=Arial];
  node [fixedsize=true, width=0.6, height=0.6];
  edge [arrowhead=vee, arrowsize=0.7];

  //b -> Y[penwidth=${10*lw(Math.abs(b))}, label=${b}]; //<-- Change the factor here
  b -> Y[penwidth=2, label=${b}];
  x1 -> Y[penwidth=2, label=${w}];
  
  x1 [label="x", pos="0,0"]; 
  b  [label="1", pos="0,2"]; 
  Y  [label="y", pos="1,0.8"]; 
}`